Machine Learning With R Training Course Online Course

Machine Learning with R Training Course

All levels
English

Course Description

The Machine Learning with R Course is for the candidates, who wants to learn algorithm coding and formula and other aspects of the data and analytics. This Machine Learning Courses are the concoction of Data Science with R, Introduction to Machine Learning, Random Forest, General Boosting & Bagging, Support Vector Machines, Neural Networks and Text Mining with R. The training insights the candidates on the syntax, variables, and types, create functions and use control flow, work with data in R. Moreover, they would be able to gain insight on regression, clustering, classification, including measuring the variable importance through permutation and gaining hands-on experience on solving the algorithm with the complexity of a classifier to gain accuracy.

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What you’ll learn

Live Class Practical Oriented Training
Timely Doubt Resolution
Dedicated Student Success Mentor
Certification & Job Assistance
Free Access to Workshop & Webinar
No Cost EMI Option
Develop an understanding of categorical variables and continuous variables, that helps in using the boosting and bagging...
Explore R language fundamentals, including basic syntax, variables, and types
How neural networks effective in image segmentation. How to use the calculus in simpler form
Understand kernel functions such as: spline kernels, linear, radial basis function and polynomial and Text Mining with R...
Why Support Vector Machines is called the most high-performing algorithm

Covering Topics

Module-1 Data Science with R

Module-2 Introduction to Machine Learning

Module-3 Random Forestv

Module-4 General Boosting & Bagging

Module-5 Support Vector Machines

Module-6 Neural Networks

Module-7 Text Mining with R

Curriculum

  Module-1 Data Science with R
Live Lecture 
·       Exploratory Data Analysis and Visualization

·       R for Data Science

·       Data Mining

·       Data Analysis for Evidence Based Decision Making

·       Industry Applications of Advanced Analytics Models

·       Big Data Analytics with Spark

·       Project Management in Analytics

·       Information to Insight

·       Career Management

  Module-2 Introduction to Machine Learning
Live Lecture 
·       An Introduction

·       The Regression Algorithms

·       The Classifiers: Bayesian and kNN

·       Tree Based Algorithms

·       SVM and Improving Performance

  Module-3 Random Forest
Live Lecture 
·       Single Decision Tree

·       Rise of Ensemble Method

·       Practical Exercises in R on Business Case Studies

  Module-4 General Boosting & Bagging
Live Lecture 
·       Decision Tree Ensembles: Bagging and Boosting

·       The Case Study: Analysis of Credit Data

·       The Case Study: The Titanic Accident

·       The Case Study: Comparing Algorithms

  Module-5 Support Vector Machines
Live Lecture 
·       Introduction to the Support Vector Machines

  Module-6 Neural Networks
Live Lecture 
·       An Introduction

·       The Perceptron learning procedure

·       The backpropagation learning procedure

·       Learning feature vectors for words

·       Object recognition with neural nets

·       Optimization: How to make the learning go faster

·       Recurrent neural networks

·       More recurrent neural networks

·       Ways to make neural networks generalize better

·       Combining multiple neural networks to improve generalization

·       Hopfield nets and Boltzmann machines

·       Restricted Boltzmann machines (RBMs)

·       Stacking RBMs to make Deep Belief Nets

·       Deep neural nets with generative pre-training

·       Modeling hierarchical structure with neural nets

·       Recent applications of deep neural nets

  Module-7 Text Mining with R
Live Lecture 
·       An Introduction to the Text Mining

·       TM Packages in R

·       Regular Expressions

·       Sentiment Analysis

·       Topic Modelling

·       Network Analysis

·       Clustering

Frequently Asked Questions

The candidates should have knowledge of the basics of programming, SQL and math and statistic concepts.

Ans: The course offers a variety of online training options, including: • Live Virtual Classroom Training: Participate in real-time interactive sessions with instructors and peers. • 1:1 Doubt Resolution Sessions: Get personalized assistance and clarification on course-related queries. • Recorded Live Lectures*: Access recorded sessions for review or to catch up on missed classes. • Flexible Schedule: Enjoy the flexibility to learn at your own pace and according to your schedule.

Ans: Live Virtual Classroom Training allows you to attend instructor-led sessions in real-time through an online platform. You can interact with the instructor, ask questions, participate in discussions, and collaborate with fellow learners, simulating the experience of a traditional classroom setting from the comfort of your own space.

Ans: If you miss a live session, you can access recorded lectures* to review the content covered during the session. This allows you to catch up on any missed material at your own pace and ensures that you don't fall behind in your learning journey.

Ans: The course offers a flexible schedule, allowing you to learn at times that suit you best. Whether you have other commitments or prefer to study during specific hours, the course structure accommodates your needs, enabling you to balance your learning with other responsibilities effectively. *Note: Availability of recorded live lectures may vary depending on the course and training provider.